We analyze the effects of better algorithmic demand forecasting on collusive profits. We show that the comparative statics crucially depend on the whether actions are observable. Thus, the optimal antitrust policy needs to take into account the institutional settings of the industry in question....

We consider a multi-service transportation system in which passengers are heterogeneous along two dimensions, namely ideal departure time and value of time, leading to both horizontal and vertical differentiation. We investigate the behavior of passengers, and assess how service pricing and...

Sophisticated collusive compensation schemes such as assigning future market shares or direct transfers are frequently observed in detected cartels. We show formally why these schemes are useful for dampening deviation incentives when colluding firms are temporary asymmetric. The relative...

Due to computing and communication facilities, formal procedures, often referred to as "algorithms", are now extensively used in public, economic and social areas. These procedures, currently at the forefront of criticisms, share some features with mechanisms as defined by economists, following...

We study the interaction between algorithmic advice and human decisions using high-resolution hotel-room pricing data. We document that price setting frictions, arising from adjustment costs of human decision makers, induce a conflict of interest with the algorithmic advisor. A model of advice...

This paper addresses the steep learning curve in Machine Learning faced by non-computer scientists, particularly social scientists, stemming from the absence of a primer on its fundamental principles. I adopt a pedagogical strategy inspired by the adage ”once you understand OLS, you can work...